Overview

Dataset statistics

Number of variables24
Number of observations12070
Missing cells184
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 MiB
Average record size in memory200.0 B

Variable types

Text1
Numeric18
Boolean4
Categorical1

Alerts

remaining_term is highly overall correlated with churnHigh correlation
number_vmail_messages is highly overall correlated with voice_mail_planHigh correlation
total_day_minutes is highly overall correlated with total_day_chargeHigh correlation
total_day_charge is highly overall correlated with total_day_minutesHigh correlation
total_eve_minutes is highly overall correlated with total_eve_chargeHigh correlation
total_eve_charge is highly overall correlated with total_eve_minutesHigh correlation
total_night_minutes is highly overall correlated with total_night_chargeHigh correlation
total_night_charge is highly overall correlated with total_night_minutesHigh correlation
total_intl_minutes is highly overall correlated with total_intl_chargeHigh correlation
total_intl_charge is highly overall correlated with total_intl_minutesHigh correlation
voice_mail_plan is highly overall correlated with number_vmail_messagesHigh correlation
churn is highly overall correlated with remaining_termHigh correlation
promotions_offered is highly imbalanced (67.2%)Imbalance
international_plan is highly imbalanced (55.6%)Imbalance
total_day_minutes has unique valuesUnique
total_day_charge has unique valuesUnique
total_eve_charge has unique valuesUnique
total_night_minutes has unique valuesUnique
total_night_charge has unique valuesUnique
total_intl_minutes has unique valuesUnique
total_intl_charge has unique valuesUnique
number_vmail_messages has 1731 (14.3%) zerosZeros
number_customer_service_calls has 2534 (21.0%) zerosZeros

Reproduction

Analysis started2023-09-04 13:47:27.078165
Analysis finished2023-09-04 13:48:19.794362
Duration52.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct51
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:19.953914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters24140
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTN
2nd rowDE
3rd rowAZ
4th rowKY
5th rowHI
ValueCountFrequency (%)
wv 398
 
3.3%
id 323
 
2.7%
mn 293
 
2.4%
or 293
 
2.4%
ut 288
 
2.4%
ny 281
 
2.3%
tx 277
 
2.3%
al 275
 
2.3%
va 272
 
2.3%
mt 267
 
2.2%
Other values (41) 9103
75.4%
2023-09-04T08:48:20.361399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 2664
 
11.0%
A 2491
 
10.3%
M 2216
 
9.2%
I 1891
 
7.8%
T 1555
 
6.4%
D 1390
 
5.8%
C 1291
 
5.3%
O 1205
 
5.0%
V 1162
 
4.8%
W 1145
 
4.7%
Other values (14) 7130
29.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 24140
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 2664
 
11.0%
A 2491
 
10.3%
M 2216
 
9.2%
I 1891
 
7.8%
T 1555
 
6.4%
D 1390
 
5.8%
C 1291
 
5.3%
O 1205
 
5.0%
V 1162
 
4.8%
W 1145
 
4.7%
Other values (14) 7130
29.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 24140
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 2664
 
11.0%
A 2491
 
10.3%
M 2216
 
9.2%
I 1891
 
7.8%
T 1555
 
6.4%
D 1390
 
5.8%
C 1291
 
5.3%
O 1205
 
5.0%
V 1162
 
4.8%
W 1145
 
4.7%
Other values (14) 7130
29.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 24140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 2664
 
11.0%
A 2491
 
10.3%
M 2216
 
9.2%
I 1891
 
7.8%
T 1555
 
6.4%
D 1390
 
5.8%
C 1291
 
5.3%
O 1205
 
5.0%
V 1162
 
4.8%
W 1145
 
4.7%
Other values (14) 7130
29.5%

tenure
Real number (ℝ)

Distinct230
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.31077
Minimum1
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:20.571034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q173
median100
Q3127
95-th percentile168
Maximum238
Range237
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.742023
Coefficient of variation (CV)0.39618899
Kurtosis-0.1587008
Mean100.31077
Median Absolute Deviation (MAD)27
Skewness0.10236269
Sum1210751
Variance1579.4284
MonotonicityNot monotonic
2023-09-04T08:48:20.771507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 145
 
1.2%
102 145
 
1.2%
117 135
 
1.1%
105 134
 
1.1%
93 131
 
1.1%
97 129
 
1.1%
101 129
 
1.1%
113 128
 
1.1%
109 125
 
1.0%
110 125
 
1.0%
Other values (220) 10744
89.0%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 11
0.1%
3 11
0.1%
4 14
0.1%
5 10
0.1%
6 12
0.1%
7 8
0.1%
8 14
0.1%
9 11
0.1%
10 10
0.1%
ValueCountFrequency (%)
238 1
 
< 0.1%
237 1
 
< 0.1%
232 2
< 0.1%
230 1
 
< 0.1%
228 3
< 0.1%
227 1
 
< 0.1%
225 1
 
< 0.1%
223 1
 
< 0.1%
222 1
 
< 0.1%
221 1
 
< 0.1%

contract_length
Real number (ℝ)

Distinct17
Distinct (%)0.1%
Missing29
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean15.92484
Minimum8
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:20.922804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile8
Q112
median16
Q320
95-th percentile24
Maximum24
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.904918
Coefficient of variation (CV)0.30800422
Kurtosis-1.2067236
Mean15.92484
Median Absolute Deviation (MAD)4
Skewness0.015308932
Sum191751
Variance24.058221
MonotonicityNot monotonic
2023-09-04T08:48:21.105356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17 740
 
6.1%
8 736
 
6.1%
9 732
 
6.1%
13 729
 
6.0%
16 725
 
6.0%
11 723
 
6.0%
10 717
 
5.9%
20 714
 
5.9%
24 705
 
5.8%
15 702
 
5.8%
Other values (7) 4818
39.9%
ValueCountFrequency (%)
8 736
6.1%
9 732
6.1%
10 717
5.9%
11 723
6.0%
12 701
5.8%
13 729
6.0%
14 669
5.5%
15 702
5.8%
16 725
6.0%
17 740
6.1%
ValueCountFrequency (%)
24 705
5.8%
23 676
5.6%
22 699
5.8%
21 686
5.7%
20 714
5.9%
19 689
5.7%
18 698
5.8%
17 740
6.1%
16 725
6.0%
15 702
5.8%

promotions_offered
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing29
Missing (%)0.2%
Memory size117.9 KiB
False
11318 
True
 
723
(Missing)
 
29
ValueCountFrequency (%)
False 11318
93.8%
True 723
 
6.0%
(Missing) 29
 
0.2%
2023-09-04T08:48:21.292962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

remaining_term
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing29
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean11.137115
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:21.439167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q317
95-th percentile23
Maximum24
Range23
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.2122118
Coefficient of variation (CV)0.6475835
Kurtosis-1.2570163
Mean11.137115
Median Absolute Deviation (MAD)6
Skewness0.24481472
Sum134102
Variance52.015999
MonotonicityNot monotonic
2023-09-04T08:48:21.611943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2 822
 
6.8%
3 770
 
6.4%
1 737
 
6.1%
7 656
 
5.4%
4 640
 
5.3%
5 609
 
5.0%
6 589
 
4.9%
21 460
 
3.8%
17 451
 
3.7%
15 448
 
3.7%
Other values (14) 5859
48.5%
ValueCountFrequency (%)
1 737
6.1%
2 822
6.8%
3 770
6.4%
4 640
5.3%
5 609
5.0%
6 589
4.9%
7 656
5.4%
8 432
3.6%
9 438
3.6%
10 438
3.6%
ValueCountFrequency (%)
24 440
3.6%
23 434
3.6%
22 402
3.3%
21 460
3.8%
20 446
3.7%
19 420
3.5%
18 402
3.3%
17 451
3.7%
16 369
3.1%
15 448
3.7%

last_nps_rating
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing29
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean6.1028154
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:21.772262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.7237225
Coefficient of variation (CV)0.44630589
Kurtosis-0.9782282
Mean6.1028154
Median Absolute Deviation (MAD)2
Skewness-0.39179404
Sum73484
Variance7.418664
MonotonicityNot monotonic
2023-09-04T08:48:21.925838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 1778
14.7%
9 1685
14.0%
7 1664
13.8%
8 1608
13.3%
10 1103
9.1%
2 930
7.7%
4 918
7.6%
3 889
7.4%
1 855
7.1%
5 611
 
5.1%
(Missing) 29
 
0.2%
ValueCountFrequency (%)
1 855
7.1%
2 930
7.7%
3 889
7.4%
4 918
7.6%
5 611
 
5.1%
6 1778
14.7%
7 1664
13.8%
8 1608
13.3%
9 1685
14.0%
10 1103
9.1%
ValueCountFrequency (%)
10 1103
9.1%
9 1685
14.0%
8 1608
13.3%
7 1664
13.8%
6 1778
14.7%
5 611
 
5.1%
4 918
7.6%
3 889
7.4%
2 930
7.7%
1 855
7.1%

area_code
Categorical

Distinct3
Distinct (%)< 0.1%
Missing7
Missing (%)0.1%
Memory size188.6 KiB
area_code_415
6049 
area_code_510
3035 
area_code_408
2979 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters156819
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarea_code_510
2nd rowarea_code_510
3rd rowarea_code_415
4th rowarea_code_408
5th rowarea_code_510

Common Values

ValueCountFrequency (%)
area_code_415 6049
50.1%
area_code_510 3035
25.1%
area_code_408 2979
24.7%
(Missing) 7
 
0.1%

Length

2023-09-04T08:48:22.101529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T08:48:22.245756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
area_code_415 6049
50.1%
area_code_510 3035
25.2%
area_code_408 2979
24.7%

Most occurring characters

ValueCountFrequency (%)
a 24126
15.4%
e 24126
15.4%
_ 24126
15.4%
r 12063
7.7%
c 12063
7.7%
o 12063
7.7%
d 12063
7.7%
1 9084
 
5.8%
5 9084
 
5.8%
4 9028
 
5.8%
Other values (2) 8993
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 96504
61.5%
Decimal Number 36189
 
23.1%
Connector Punctuation 24126
 
15.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 24126
25.0%
e 24126
25.0%
r 12063
12.5%
c 12063
12.5%
o 12063
12.5%
d 12063
12.5%
Decimal Number
ValueCountFrequency (%)
1 9084
25.1%
5 9084
25.1%
4 9028
24.9%
0 6014
16.6%
8 2979
 
8.2%
Connector Punctuation
ValueCountFrequency (%)
_ 24126
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 96504
61.5%
Common 60315
38.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 24126
25.0%
e 24126
25.0%
r 12063
12.5%
c 12063
12.5%
o 12063
12.5%
d 12063
12.5%
Common
ValueCountFrequency (%)
_ 24126
40.0%
1 9084
 
15.1%
5 9084
 
15.1%
4 9028
 
15.0%
0 6014
 
10.0%
8 2979
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 24126
15.4%
e 24126
15.4%
_ 24126
15.4%
r 12063
7.7%
c 12063
7.7%
o 12063
7.7%
d 12063
7.7%
1 9084
 
5.8%
5 9084
 
5.8%
4 9028
 
5.8%
Other values (2) 8993
 
5.7%

international_plan
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size106.1 KiB
False
10957 
True
1113 
ValueCountFrequency (%)
False 10957
90.8%
True 1113
 
9.2%
2023-09-04T08:48:22.372697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

voice_mail_plan
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing20
Missing (%)0.2%
Memory size117.9 KiB
False
8887 
True
3163 
(Missing)
 
20
ValueCountFrequency (%)
False 8887
73.6%
True 3163
 
26.2%
(Missing) 20
 
0.2%
2023-09-04T08:48:22.505820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

number_vmail_messages
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6386081
Minimum0
Maximum52
Zeros1731
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:22.678528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q316
95-th percentile36
Maximum52
Range52
Interquartile range (IQR)15

Descriptive statistics

Standard deviation13.030305
Coefficient of variation (CV)1.5083801
Kurtosis0.33147233
Mean8.6386081
Median Absolute Deviation (MAD)1
Skewness1.3790256
Sum104268
Variance169.78885
MonotonicityNot monotonic
2023-09-04T08:48:22.882601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 3464
28.7%
2 3359
27.8%
0 1731
14.3%
3 349
 
2.9%
30 207
 
1.7%
31 185
 
1.5%
29 180
 
1.5%
28 174
 
1.4%
27 171
 
1.4%
26 139
 
1.2%
Other values (40) 2111
17.5%
ValueCountFrequency (%)
0 1731
14.3%
1 3464
28.7%
2 3359
27.8%
3 349
 
2.9%
6 4
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
10 8
 
0.1%
11 12
 
0.1%
12 5
 
< 0.1%
ValueCountFrequency (%)
52 4
 
< 0.1%
51 2
 
< 0.1%
50 5
 
< 0.1%
49 10
 
0.1%
48 8
 
0.1%
47 14
 
0.1%
46 17
0.1%
45 18
0.1%
44 42
0.3%
43 38
0.3%

total_day_minutes
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct12070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean179.82137
Minimum1.3839017
Maximum351.09182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:23.086188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3839017
5-th percentile89.74897
Q1142.62117
median179.70508
Q3217.02436
95-th percentile271.25486
Maximum351.09182
Range349.70792
Interquartile range (IQR)74.403187

Descriptive statistics

Standard deviation54.694309
Coefficient of variation (CV)0.30415912
Kurtosis-0.044252426
Mean179.82137
Median Absolute Deviation (MAD)37.186918
Skewness0.021166821
Sum2170443.9
Variance2991.4675
MonotonicityNot monotonic
2023-09-04T08:48:23.294087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.2649102 1
 
< 0.1%
123.4344043 1
 
< 0.1%
166.2388228 1
 
< 0.1%
244.2079738 1
 
< 0.1%
145.2743933 1
 
< 0.1%
171.267347 1
 
< 0.1%
209.7171232 1
 
< 0.1%
73.54513394 1
 
< 0.1%
215.0982239 1
 
< 0.1%
97.1380356 1
 
< 0.1%
Other values (12060) 12060
99.9%
ValueCountFrequency (%)
1.383901705 1
< 0.1%
2.253772021 1
< 0.1%
3.276387559 1
< 0.1%
5.274037576 1
< 0.1%
5.684070477 1
< 0.1%
5.900453333 1
< 0.1%
6.681255122 1
< 0.1%
8.729202756 1
< 0.1%
9.059645578 1
< 0.1%
10.29086229 1
< 0.1%
ValueCountFrequency (%)
351.0918227 1
< 0.1%
350.7728884 1
< 0.1%
348.1106457 1
< 0.1%
347.5604267 1
< 0.1%
347.1849395 1
< 0.1%
346.3442503 1
< 0.1%
345.3269043 1
< 0.1%
344.7302571 1
< 0.1%
344.1290277 1
< 0.1%
343.0510698 1
< 0.1%

total_day_calls
Real number (ℝ)

Distinct138
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.06437
Minimum1
Maximum165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:23.498071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range164
Interquartile range (IQR)26

Descriptive statistics

Standard deviation20.137844
Coefficient of variation (CV)0.20124889
Kurtosis0.14995377
Mean100.06437
Median Absolute Deviation (MAD)13
Skewness-0.081698159
Sum1207777
Variance405.53277
MonotonicityNot monotonic
2023-09-04T08:48:23.714165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101 282
 
2.3%
106 273
 
2.3%
99 269
 
2.2%
105 268
 
2.2%
102 260
 
2.2%
103 258
 
2.1%
92 254
 
2.1%
100 251
 
2.1%
96 246
 
2.0%
107 246
 
2.0%
Other values (128) 9463
78.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
6 1
 
< 0.1%
8 1
 
< 0.1%
28 1
 
< 0.1%
34 2
< 0.1%
35 2
< 0.1%
36 1
 
< 0.1%
37 3
< 0.1%
ValueCountFrequency (%)
165 2
 
< 0.1%
164 2
 
< 0.1%
163 3
< 0.1%
162 5
< 0.1%
161 4
< 0.1%
160 1
 
< 0.1%
159 1
 
< 0.1%
158 3
< 0.1%
157 1
 
< 0.1%
156 6
< 0.1%

total_day_charge
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct12070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.574659
Minimum0.11960265
Maximum59.727092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:23.918132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.11960265
5-th percentile15.315147
Q124.323777
median30.517433
Q336.834662
95-th percentile46.233877
Maximum59.727092
Range59.60749
Interquartile range (IQR)12.510885

Descriptive statistics

Standard deviation9.3011677
Coefficient of variation (CV)0.30421166
Kurtosis-0.037926791
Mean30.574659
Median Absolute Deviation (MAD)6.2642704
Skewness0.019023804
Sum369036.13
Variance86.51172
MonotonicityNot monotonic
2023-09-04T08:48:24.121692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.26514717 1
 
< 0.1%
23.31548417 1
 
< 0.1%
26.97607021 1
 
< 0.1%
40.43196168 1
 
< 0.1%
24.75346032 1
 
< 0.1%
28.73117049 1
 
< 0.1%
35.41486318 1
 
< 0.1%
12.30274778 1
 
< 0.1%
37.48230428 1
 
< 0.1%
17.10542875 1
 
< 0.1%
Other values (12060) 12060
99.9%
ValueCountFrequency (%)
0.11960265 1
< 0.1%
0.199583532 1
< 0.1%
0.394549988 1
< 0.1%
0.420358932 1
< 0.1%
0.633176091 1
< 0.1%
0.701549148 1
< 0.1%
1.16159554 1
< 0.1%
1.16639652 1
< 0.1%
1.347704568 1
< 0.1%
1.359332465 1
< 0.1%
ValueCountFrequency (%)
59.72709248 1
< 0.1%
59.61329115 1
< 0.1%
59.5047015 1
< 0.1%
59.49638423 1
< 0.1%
59.27803871 1
< 0.1%
59.1434571 1
< 0.1%
59.13948553 1
< 0.1%
58.64323971 1
< 0.1%
58.60394924 1
< 0.1%
58.45380136 1
< 0.1%

total_eve_minutes
Real number (ℝ)

HIGH CORRELATION 

Distinct12058
Distinct (%)100.0%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean200.2883
Minimum7.6054892
Maximum357.73943
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:24.326099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7.6054892
5-th percentile116.37218
Q1166.71923
median200.74681
Q3233.94315
95-th percentile283.49604
Maximum357.73943
Range350.13394
Interquartile range (IQR)67.223925

Descriptive statistics

Standard deviation50.899001
Coefficient of variation (CV)0.25412868
Kurtosis0.034319772
Mean200.2883
Median Absolute Deviation (MAD)33.653665
Skewness-0.034996402
Sum2415076.4
Variance2590.7083
MonotonicityNot monotonic
2023-09-04T08:48:24.530229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226.3256044 1
 
< 0.1%
209.4960581 1
 
< 0.1%
188.0054205 1
 
< 0.1%
311.5940276 1
 
< 0.1%
167.8863048 1
 
< 0.1%
285.6272746 1
 
< 0.1%
215.5572808 1
 
< 0.1%
93.89228975 1
 
< 0.1%
171.3260714 1
 
< 0.1%
116.9034619 1
 
< 0.1%
Other values (12048) 12048
99.8%
(Missing) 12
 
0.1%
ValueCountFrequency (%)
7.605489225 1
< 0.1%
13.87460294 1
< 0.1%
29.7904916 1
< 0.1%
33.59766632 1
< 0.1%
37.57979758 1
< 0.1%
37.67775095 1
< 0.1%
37.96739213 1
< 0.1%
38.61739428 1
< 0.1%
38.74471285 1
< 0.1%
39.06182054 1
< 0.1%
ValueCountFrequency (%)
357.7394287 1
< 0.1%
357.1613524 1
< 0.1%
356.845887 1
< 0.1%
356.5855219 1
< 0.1%
356.0424462 1
< 0.1%
355.9443647 1
< 0.1%
355.882914 1
< 0.1%
355.1030965 1
< 0.1%
354.8768408 1
< 0.1%
354.163619 1
< 0.1%

total_eve_calls
Real number (ℝ)

Distinct130
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.41698
Minimum1
Maximum169
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:24.748354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile68
Q187
median101
Q3114
95-th percentile134
Maximum169
Range168
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.898596
Coefficient of variation (CV)0.19815967
Kurtosis0.030601163
Mean100.41698
Median Absolute Deviation (MAD)14
Skewness-0.017197949
Sum1212033
Variance395.95412
MonotonicityNot monotonic
2023-09-04T08:48:24.952325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105 259
 
2.1%
104 256
 
2.1%
110 253
 
2.1%
107 252
 
2.1%
87 241
 
2.0%
109 241
 
2.0%
106 240
 
2.0%
108 240
 
2.0%
103 239
 
2.0%
100 239
 
2.0%
Other values (120) 9610
79.6%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
9 1
 
< 0.1%
16 2
 
< 0.1%
36 1
 
< 0.1%
37 1
 
< 0.1%
39 1
 
< 0.1%
42 1
 
< 0.1%
43 6
< 0.1%
44 4
< 0.1%
ValueCountFrequency (%)
169 1
 
< 0.1%
165 1
 
< 0.1%
162 2
 
< 0.1%
161 2
 
< 0.1%
160 2
 
< 0.1%
159 4
< 0.1%
158 2
 
< 0.1%
157 4
< 0.1%
156 6
< 0.1%
155 5
< 0.1%

total_eve_charge
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct12070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.014347
Minimum1.0526206
Maximum30.474658
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:25.155513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.0526206
5-th percentile9.9419076
Q114.152096
median17.049083
Q319.870764
95-th percentile24.070794
Maximum30.474658
Range29.422038
Interquartile range (IQR)5.7186676

Descriptive statistics

Standard deviation4.3198071
Coefficient of variation (CV)0.25389204
Kurtosis0.019455923
Mean17.014347
Median Absolute Deviation (MAD)2.8622218
Skewness-0.033209868
Sum205363.16
Variance18.660733
MonotonicityNot monotonic
2023-09-04T08:48:25.377115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.92798711 1
 
< 0.1%
9.149633085 1
 
< 0.1%
18.54748163 1
 
< 0.1%
19.70218451 1
 
< 0.1%
10.55262594 1
 
< 0.1%
13.72531646 1
 
< 0.1%
16.44329767 1
 
< 0.1%
26.19043321 1
 
< 0.1%
12.81802111 1
 
< 0.1%
23.0298188 1
 
< 0.1%
Other values (12060) 12060
99.9%
ValueCountFrequency (%)
1.052620602 1
< 0.1%
1.082034259 1
< 0.1%
1.781883509 1
< 0.1%
2.575885559 1
< 0.1%
3.116535494 1
< 0.1%
3.142057001 1
< 0.1%
3.198248011 1
< 0.1%
3.227988872 1
< 0.1%
3.310988396 1
< 0.1%
3.328986687 1
< 0.1%
ValueCountFrequency (%)
30.47465846 1
< 0.1%
30.42237227 1
< 0.1%
30.31675528 1
< 0.1%
30.22450951 1
< 0.1%
30.18550066 1
< 0.1%
30.13484407 1
< 0.1%
30.06371552 1
< 0.1%
30.00957842 1
< 0.1%
29.96673582 1
< 0.1%
29.870605 1
< 0.1%

total_night_minutes
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct12070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199.54625
Minimum4.2477068
Maximum394.85105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:25.583135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.2477068
5-th percentile116.75517
Q1165.68363
median198.63569
Q3234.23699
95-th percentile283.96667
Maximum394.85105
Range390.60335
Interquartile range (IQR)68.553365

Descriptive statistics

Standard deviation50.871813
Coefficient of variation (CV)0.25493745
Kurtosis0.064467625
Mean199.54625
Median Absolute Deviation (MAD)34.306972
Skewness0.020780417
Sum2408523.3
Variance2587.9413
MonotonicityNot monotonic
2023-09-04T08:48:25.798809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
217.0026187 1
 
< 0.1%
155.2447967 1
 
< 0.1%
235.3958858 1
 
< 0.1%
187.5315096 1
 
< 0.1%
247.2747071 1
 
< 0.1%
80.59613409 1
 
< 0.1%
167.5101711 1
 
< 0.1%
122.6801833 1
 
< 0.1%
233.1131687 1
 
< 0.1%
168.1683756 1
 
< 0.1%
Other values (12060) 12060
99.9%
ValueCountFrequency (%)
4.247706833 1
< 0.1%
14.36778219 1
< 0.1%
19.87057585 1
< 0.1%
23.68633147 1
< 0.1%
32.2343525 1
< 0.1%
37.01650076 1
< 0.1%
39.75705461 1
< 0.1%
42.55363244 1
< 0.1%
42.6292883 1
< 0.1%
42.87293722 1
< 0.1%
ValueCountFrequency (%)
394.851052 1
< 0.1%
394.5692948 1
< 0.1%
394.319131 1
< 0.1%
393.7473654 1
< 0.1%
392.0817771 1
< 0.1%
389.3467599 1
< 0.1%
385.5243677 1
< 0.1%
385.0642105 1
< 0.1%
379.7519088 1
< 0.1%
379.4195785 1
< 0.1%

total_night_calls
Real number (ℝ)

Distinct136
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.791881
Minimum1
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:26.002944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile67
Q186
median99
Q3114
95-th percentile131
Maximum173
Range172
Interquartile range (IQR)28

Descriptive statistics

Standard deviation20.195931
Coefficient of variation (CV)0.20238051
Kurtosis-0.030889075
Mean99.791881
Median Absolute Deviation (MAD)14
Skewness0.024950491
Sum1204488
Variance407.87565
MonotonicityNot monotonic
2023-09-04T08:48:26.221607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96 246
 
2.0%
95 242
 
2.0%
91 240
 
2.0%
97 239
 
2.0%
99 238
 
2.0%
90 235
 
1.9%
89 232
 
1.9%
108 231
 
1.9%
88 231
 
1.9%
94 226
 
1.9%
Other values (126) 9710
80.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
4 1
 
< 0.1%
33 1
 
< 0.1%
35 1
 
< 0.1%
36 4
< 0.1%
37 2
 
< 0.1%
38 2
 
< 0.1%
39 1
 
< 0.1%
40 5
< 0.1%
41 3
< 0.1%
ValueCountFrequency (%)
173 1
 
< 0.1%
169 1
 
< 0.1%
166 2
 
< 0.1%
165 5
< 0.1%
164 1
 
< 0.1%
163 1
 
< 0.1%
162 2
 
< 0.1%
161 2
 
< 0.1%
160 1
 
< 0.1%
159 2
 
< 0.1%

total_night_charge
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct12070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.9790099
Minimum0.47232007
Maximum17.700351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:26.440717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.47232007
5-th percentile5.2344575
Q17.4462096
median8.9529964
Q310.520554
95-th percentile12.789577
Maximum17.700351
Range17.228031
Interquartile range (IQR)3.0743442

Descriptive statistics

Standard deviation2.2852685
Coefficient of variation (CV)0.2545123
Kurtosis0.05812154
Mean8.9790099
Median Absolute Deviation (MAD)1.536607
Skewness0.014980644
Sum108376.65
Variance5.2224521
MonotonicityNot monotonic
2023-09-04T08:48:26.644697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.332900446 1
 
< 0.1%
6.27481455 1
 
< 0.1%
10.2285772 1
 
< 0.1%
8.803309674 1
 
< 0.1%
11.20344381 1
 
< 0.1%
4.074630795 1
 
< 0.1%
7.181759213 1
 
< 0.1%
5.522452897 1
 
< 0.1%
10.23103917 1
 
< 0.1%
7.717290516 1
 
< 0.1%
Other values (12060) 12060
99.9%
ValueCountFrequency (%)
0.472320072 1
< 0.1%
0.570488102 1
< 0.1%
1.333742668 1
< 0.1%
1.551844264 1
< 0.1%
1.658313241 1
< 0.1%
1.71601519 1
< 0.1%
1.77919794 1
< 0.1%
1.783760695 1
< 0.1%
1.837943137 1
< 0.1%
1.856596591 1
< 0.1%
ValueCountFrequency (%)
17.70035068 1
< 0.1%
17.58045327 1
< 0.1%
17.51801587 1
< 0.1%
17.44488275 1
< 0.1%
17.30818936 1
< 0.1%
17.22973055 1
< 0.1%
17.21201518 1
< 0.1%
17.12088707 1
< 0.1%
17.01766255 1
< 0.1%
16.97833699 1
< 0.1%

total_intl_minutes
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct12070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.300118
Minimum0.032251626
Maximum19.981388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:26.864845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.032251626
5-th percentile5.6426833
Q18.5294747
median10.38298
Q312.066125
95-th percentile14.7662
Maximum19.981388
Range19.949136
Interquartile range (IQR)3.53665

Descriptive statistics

Standard deviation2.8048257
Coefficient of variation (CV)0.27231006
Kurtosis0.62058194
Mean10.300118
Median Absolute Deviation (MAD)1.7664048
Skewness-0.20854323
Sum124322.42
Variance7.8670471
MonotonicityNot monotonic
2023-09-04T08:48:27.089265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.308312365 1
 
< 0.1%
9.939059641 1
 
< 0.1%
10.27365593 1
 
< 0.1%
6.044009466 1
 
< 0.1%
6.403711513 1
 
< 0.1%
13.56040628 1
 
< 0.1%
7.092965864 1
 
< 0.1%
9.887069262 1
 
< 0.1%
14.57449194 1
 
< 0.1%
8.903608503 1
 
< 0.1%
Other values (12060) 12060
99.9%
ValueCountFrequency (%)
0.032251626 1
< 0.1%
0.032854688 1
< 0.1%
0.04510787 1
< 0.1%
0.04861084 1
< 0.1%
0.057384465 1
< 0.1%
0.074933138 1
< 0.1%
0.097893899 1
< 0.1%
0.099603693 1
< 0.1%
0.101293308 1
< 0.1%
0.11626781 1
< 0.1%
ValueCountFrequency (%)
19.98138775 1
< 0.1%
19.97419604 1
< 0.1%
19.97366835 1
< 0.1%
19.93003112 1
< 0.1%
19.78836333 1
< 0.1%
19.75557665 1
< 0.1%
19.75353643 1
< 0.1%
19.74898858 1
< 0.1%
19.72924614 1
< 0.1%
19.51103528 1
< 0.1%

total_intl_calls
Real number (ℝ)

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9618061
Minimum0
Maximum20
Zeros35
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:27.288634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median5
Q36
95-th percentile10
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5086047
Coefficient of variation (CV)0.50558297
Kurtosis2.8480294
Mean4.9618061
Median Absolute Deviation (MAD)2
Skewness1.2349803
Sum59889
Variance6.2930974
MonotonicityNot monotonic
2023-09-04T08:48:27.476373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4 2223
18.4%
3 2134
17.7%
5 2043
16.9%
6 1483
12.3%
2 1158
9.6%
7 953
7.9%
8 640
 
5.3%
9 409
 
3.4%
1 360
 
3.0%
10 279
 
2.3%
Other values (11) 388
 
3.2%
ValueCountFrequency (%)
0 35
 
0.3%
1 360
 
3.0%
2 1158
9.6%
3 2134
17.7%
4 2223
18.4%
5 2043
16.9%
6 1483
12.3%
7 953
7.9%
8 640
 
5.3%
9 409
 
3.4%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 9
 
0.1%
18 8
 
0.1%
17 10
 
0.1%
16 28
 
0.2%
15 12
 
0.1%
14 31
 
0.3%
13 52
 
0.4%
12 70
0.6%
11 131
1.1%

total_intl_charge
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct12070
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7747432
Minimum0.013477816
Maximum5.3983334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:27.680858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.013477816
5-th percentile1.5280225
Q12.2997557
median2.8121032
Q33.2568424
95-th percentile3.9358047
Maximum5.3983334
Range5.3848556
Interquartile range (IQR)0.95708677

Descriptive statistics

Standard deviation0.75010467
Coefficient of variation (CV)0.27033301
Kurtosis0.64719842
Mean2.7747432
Median Absolute Deviation (MAD)0.4778826
Skewness-0.22691053
Sum33491.151
Variance0.56265702
MonotonicityNot monotonic
2023-09-04T08:48:27.883963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.20875943 1
 
< 0.1%
2.540903835 1
 
< 0.1%
2.810695907 1
 
< 0.1%
1.834242907 1
 
< 0.1%
1.765160989 1
 
< 0.1%
3.531968387 1
 
< 0.1%
2.066167697 1
 
< 0.1%
2.651455696 1
 
< 0.1%
3.55782044 1
 
< 0.1%
2.403567971 1
 
< 0.1%
Other values (12060) 12060
99.9%
ValueCountFrequency (%)
0.013477816 1
< 0.1%
0.018328444 1
< 0.1%
0.019205875 1
< 0.1%
0.019448916 1
< 0.1%
0.020782396 1
< 0.1%
0.028324033 1
< 0.1%
0.036756377 1
< 0.1%
0.046726288 1
< 0.1%
0.052583999 1
< 0.1%
0.052728383 1
< 0.1%
ValueCountFrequency (%)
5.398333389 1
< 0.1%
5.36014793 1
< 0.1%
5.356623683 1
< 0.1%
5.343838124 1
< 0.1%
5.337579587 1
< 0.1%
5.321647452 1
< 0.1%
5.250506757 1
< 0.1%
5.245090236 1
< 0.1%
5.243483508 1
< 0.1%
5.242569384 1
< 0.1%

number_customer_service_calls
Real number (ℝ)

ZEROS 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5553438
Minimum0
Maximum9
Zeros2534
Zeros (%)21.0%
Negative0
Negative (%)0.0%
Memory size188.6 KiB
2023-09-04T08:48:28.040655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3178485
Coefficient of variation (CV)0.84730365
Kurtosis1.5964357
Mean1.5553438
Median Absolute Deviation (MAD)1
Skewness1.0867767
Sum18773
Variance1.7367247
MonotonicityNot monotonic
2023-09-04T08:48:28.199495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 4382
36.3%
2 2615
21.7%
0 2534
21.0%
3 1548
 
12.8%
4 627
 
5.2%
5 239
 
2.0%
6 78
 
0.6%
7 36
 
0.3%
9 6
 
< 0.1%
8 5
 
< 0.1%
ValueCountFrequency (%)
0 2534
21.0%
1 4382
36.3%
2 2615
21.7%
3 1548
 
12.8%
4 627
 
5.2%
5 239
 
2.0%
6 78
 
0.6%
7 36
 
0.3%
8 5
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
9 6
 
< 0.1%
8 5
 
< 0.1%
7 36
 
0.3%
6 78
 
0.6%
5 239
 
2.0%
4 627
 
5.2%
3 1548
 
12.8%
2 2615
21.7%
1 4382
36.3%
0 2534
21.0%

churn
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing29
Missing (%)0.2%
Memory size117.9 KiB
False
10314 
True
1727 
(Missing)
 
29
ValueCountFrequency (%)
False 10314
85.5%
True 1727
 
14.3%
(Missing) 29
 
0.2%
2023-09-04T08:48:28.338626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-09-04T08:48:15.685829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:29.250194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:31.859857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:34.667389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:37.269980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:39.998605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:42.586788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:45.248501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:48.220023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:50.833288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:53.514514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:56.488083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:59.159983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:01.888224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:04.548139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:07.652148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:10.386277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:13.122357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:15.811262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:29.486687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:32.001305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:34.797914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:37.407295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:40.124907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:42.728976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:45.384614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:48.344849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:50.974354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:53.640337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:56.618794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:59.285238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:02.013657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:05.039281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:07.783191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:10.527342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:13.260489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:15.968350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:29.634691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:32.159433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:34.934929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:37.552829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:40.281607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:42.884494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:45.534513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:48.517522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:51.129128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:53.797029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:56.775881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:59.441820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:02.184685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:05.201603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:07.952378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:10.689146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:13.405038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:16.111027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:29.760200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:32.300548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:35.079343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:37.693888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:40.412052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:43.025557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:45.666272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:48.654572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:51.272897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:53.938021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:56.916943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:59.582887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:02.328058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:05.349672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:08.100565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:10.837278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:13.539965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:16.251414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:29.885632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:32.436071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:35.205468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:37.819708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:40.551522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:43.170069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:45.807334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:48.795635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:51.398329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:54.082282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:57.062994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:59.723955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:02.467822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:05.484234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:08.240999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:10.966179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:13.678293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:16.408253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:30.031173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:32.570017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:35.342582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:37.956001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:40.673581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:43.307137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:46.237068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:48.937083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:51.555351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:54.216502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:57.188241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:59.881033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:02.608881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:05.632004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:08.378456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:11.121149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:13.803727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:16.549316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:30.156409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:32.739932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:35.483638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:38.104512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:40.833361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:43.449084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:46.431033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:49.086722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:51.696407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:54.660327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:57.342208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:00.022215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:02.765755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:05.789100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:08.553329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:11.278239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:13.960418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:16.690364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:30.298069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:32.880991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:35.644474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:38.236904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:40.971803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:43.605895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:46.584418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:49.218974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:51.838131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:54.801389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:57.498944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:00.185467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:02.907315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:05.929896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:08.704588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:11.436562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:14.102085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:16.831421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:30.439010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:33.022480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:35.785528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:38.377968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:41.125605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:43.747350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:46.741508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:49.360531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:51.994823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:54.956034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:57.639492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:00.343203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:03.049386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:06.091597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:08.853975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:11.578009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:14.230697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:16.972874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:30.576478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:33.193725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:35.923482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:38.524671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:41.268486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:43.888065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:46.882581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:49.501590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:52.156426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:55.107284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:57.795784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:00.492776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:03.200265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:06.250869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:09.007550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:11.734700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:14.372151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:17.120724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:30.718082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:33.334736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:36.064677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:38.681751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:41.413552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:44.045147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:47.040227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:49.655262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:52.306632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:55.248126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:57.952863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:00.652611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:03.357126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:06.423194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:09.168553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:11.890845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:14.538543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:17.269342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:30.859147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:33.475801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:36.218421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:38.809692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:41.550995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:44.185608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:47.185826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:49.799403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:52.448091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:55.389187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:58.101879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:00.805985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:03.502305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:06.576427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:09.318858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:12.032481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:14.666818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:17.868781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:31.016232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:33.648515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:36.367851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:38.964260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:41.714197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:44.351864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:47.327285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:49.940853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:52.604785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:55.537395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:58.249952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:00.947442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:03.659383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:06.737161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:09.475949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:12.199579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:14.823947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:18.022364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:31.155851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:33.916523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:36.507229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:39.116831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:41.866357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:44.500005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:47.485329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:50.107579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:52.761871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:55.698203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:58.391095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:01.106929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:03.800501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:06.890488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:09.617008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:12.356365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:14.965524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:18.169047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:31.313098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:34.076642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:36.660229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:39.423569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:42.014810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:44.641464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:47.634250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:50.253253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:52.919664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:55.855002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:58.563916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:01.273945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:03.957210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:07.031734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:09.782297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:12.501802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:15.121441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:18.319094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:31.454163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:34.235499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:36.817314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:39.590661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:42.170068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:44.813783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:47.790939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:50.394473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:53.079942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:56.007243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:58.719873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:01.433454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:04.106559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:07.187058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:09.930540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:12.675638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:15.264509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:18.469167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:31.607988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:34.385382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:36.972908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:39.732112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:42.320299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:44.969387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:47.948030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:50.555265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:53.232331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:56.174291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:58.876957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:01.590534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:04.263022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:07.359776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:10.100435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:12.831215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:15.419581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:18.601621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:31.736889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:34.526841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:37.125649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:39.857542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:42.461359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:45.123070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:48.090358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:50.705359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:53.373455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:56.347003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:47:59.018015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:01.744613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:04.404090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:07.500900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:10.230980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:12.973315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-09-04T08:48:15.548900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-09-04T08:48:28.464054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
tenurecontract_lengthremaining_termlast_nps_ratingnumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callspromotions_offeredarea_codeinternational_planvoice_mail_planchurn
tenure1.000-0.006-0.0070.008-0.020-0.0060.024-0.007-0.0180.000-0.019-0.0010.0140.0010.0120.0210.015-0.0090.0250.0450.0350.0210.031
contract_length-0.0061.0000.012-0.018-0.0060.005-0.0010.0030.009-0.0000.0100.005-0.0140.003-0.006-0.018-0.0090.0080.0180.0120.0190.0000.000
remaining_term-0.0070.0121.0000.2060.020-0.0870.009-0.087-0.0340.002-0.034-0.0180.006-0.018-0.0230.006-0.023-0.0700.3330.0090.1180.0450.519
last_nps_rating0.008-0.0180.2061.0000.019-0.0710.002-0.069-0.0500.024-0.049-0.019-0.003-0.017-0.0250.022-0.024-0.0480.2360.0000.1100.0380.494
number_vmail_messages-0.020-0.0060.0200.0191.0000.0030.0000.0040.0150.0050.0160.0060.0180.0070.011-0.0070.012-0.0210.0300.0200.0250.9990.095
total_day_minutes-0.0060.005-0.087-0.0710.0031.0000.0010.990-0.0120.022-0.0110.005-0.0070.004-0.020-0.005-0.022-0.0080.1400.0590.0640.0450.356
total_day_calls0.024-0.0010.0090.0020.0000.0011.0000.0040.019-0.0100.020-0.0040.007-0.002-0.0030.001-0.002-0.0100.0050.0380.0420.0460.039
total_day_charge-0.0070.003-0.087-0.0690.0040.9900.0041.000-0.0120.022-0.0110.005-0.0060.003-0.021-0.006-0.023-0.0060.1400.0590.0640.0490.357
total_eve_minutes-0.0180.009-0.034-0.0500.015-0.0120.019-0.0121.0000.0000.987-0.0170.012-0.0170.0100.0250.011-0.0050.0330.0350.0330.0260.079
total_eve_calls0.000-0.0000.0020.0240.0050.022-0.0100.0220.0001.0000.0000.023-0.0010.023-0.0280.006-0.0270.0150.0000.0160.0120.0200.037
total_eve_charge-0.0190.010-0.034-0.0490.016-0.0110.020-0.0110.9870.0001.000-0.0140.012-0.0140.0100.0230.010-0.0040.0290.0360.0340.0310.079
total_night_minutes-0.0010.005-0.018-0.0190.0060.005-0.0040.005-0.0170.023-0.0141.000-0.0140.9860.006-0.0050.0040.0010.0250.0390.0270.0340.054
total_night_calls0.014-0.0140.006-0.0030.018-0.0070.007-0.0060.012-0.0010.012-0.0141.000-0.0140.0100.0090.0100.0040.0000.0370.0290.0630.020
total_night_charge0.0010.003-0.018-0.0170.0070.004-0.0020.003-0.0170.023-0.0140.986-0.0141.0000.005-0.0030.0020.0030.0310.0390.0380.0270.059
total_intl_minutes0.012-0.006-0.023-0.0250.011-0.020-0.003-0.0210.010-0.0280.0100.0060.0100.0051.0000.0160.986-0.0010.0180.0380.0390.0240.056
total_intl_calls0.021-0.0180.0060.022-0.007-0.0050.001-0.0060.0250.0060.023-0.0050.009-0.0030.0161.0000.017-0.0300.0000.0190.0300.0000.052
total_intl_charge0.015-0.009-0.023-0.0240.012-0.022-0.002-0.0230.011-0.0270.0100.0040.0100.0020.9860.0171.000-0.0010.0210.0370.0410.0270.061
number_customer_service_calls-0.0090.008-0.070-0.048-0.021-0.008-0.010-0.006-0.0050.015-0.0040.0010.0040.003-0.001-0.030-0.0011.0000.0950.0430.0270.0550.257
promotions_offered0.0250.0180.3330.2360.0300.1400.0050.1400.0330.0000.0290.0250.0000.0310.0180.0000.0210.0951.0000.0000.1360.0440.495
area_code0.0450.0120.0090.0000.0200.0590.0380.0590.0350.0160.0360.0390.0370.0390.0380.0190.0370.0430.0001.0000.0230.0150.008
international_plan0.0350.0190.1180.1100.0250.0640.0420.0640.0330.0120.0340.0270.0290.0380.0390.0300.0410.0270.1360.0231.0000.0000.211
voice_mail_plan0.0210.0000.0450.0380.9990.0450.0460.0490.0260.0200.0310.0340.0630.0270.0240.0000.0270.0550.0440.0150.0001.0000.089
churn0.0310.0000.5190.4940.0950.3560.0390.3570.0790.0370.0790.0540.0200.0590.0560.0520.0610.2570.4950.0080.2110.0891.000

Missing values

2023-09-04T08:48:18.837052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-04T08:48:19.315757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-04T08:48:19.637275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

state_codetenurecontract_lengthpromotions_offeredremaining_termlast_nps_ratingarea_codeinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
id
16692TN10018.0No23.09.0area_code_510nono2117.2649108020.265147226.32560413918.927987217.002619709.3329008.30831292.2087592no
6970DE9215.0No7.07.0area_code_510nono0216.21392310636.586609190.88095012416.126664224.2640148910.2630078.23864522.1687791yes
9747AZ6210.0No18.010.0area_code_415noyes20170.45933512727.000064235.12496510920.911572123.234534875.73159711.08416953.1536710no
16099KY14312.0No12.06.0area_code_408nono1131.68197012023.501980205.2190419117.844030215.835004919.40757619.98138845.2425690no
8833HI12319.0No23.07.0area_code_510nono0139.78813510523.576148153.22124410312.536175246.69487313010.80037711.67080893.2057462no
17100KY1709.0No23.02.0area_code_408nono1124.4229267323.87356283.1346041046.240429218.04776612110.08834111.00551263.2165241no
2572ND9811.0No19.06.0area_code_415nono2112.80122213720.025215231.95494811018.330410277.82973911912.61437710.85846762.8546981no
3584NM18210.0No3.03.0area_code_408noyes30178.7901839530.295836292.0809119725.563228166.0948671187.51515915.58355714.2850753no
5247NV9521.0No20.02.0area_code_415nono1202.05689014734.388799234.07266611620.509642195.362072678.37895610.66481942.8897181no
2164IN12315.0Yes1.06.0area_code_408nono2173.7517209827.988625163.41452010914.598007241.6227967611.31040311.42202623.0705231no
state_codetenurecontract_lengthpromotions_offeredremaining_termlast_nps_ratingarea_codeinternational_planvoice_mail_plannumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_eve_chargetotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_callschurn
id
13686IL12424.0No4.07.0area_code_415nono0141.7116339625.763838264.2353699522.153979266.12599010012.29801611.06509643.2132293no
7962WV3813.0No24.010.0area_code_408nono1234.16870612139.105097195.53052812716.407778120.3638761045.6955779.80537142.6304211no
8060VT15511.0No17.06.0area_code_510nono1165.6410457127.494460215.3696274718.039833168.392906897.7243145.32769921.4844013no
7916OR16014.0No23.02.0area_code_510nono1106.8092599420.182872213.03643610117.526924271.82673110811.81428812.04060253.5039690no
1182MO10815.0No6.02.0area_code_415noyes4187.8257227012.116359183.46222410316.537779235.39060011710.5640994.54556751.2886342no
8222MS14510.0No5.06.0area_code_510nono1145.32567711826.130730195.3279138716.510144177.148800887.97018910.76149152.8366731no
9449AL30NaNNaNNaNNaNarea_code_510nono0240.9843078339.239845189.8810098816.329474155.550342556.5484216.77479721.8851430NaN
8471OH16515.0No16.010.0area_code_510nono2163.4628899927.203479198.72760512718.274905198.1423241259.6722728.79567792.1906171no
17048PA15118.0No3.09.0area_code_415nono2184.0059608132.250304211.70038612517.824783231.717911889.81706110.53562052.7626392no
1318IL10724.0No10.01.0area_code_510nono2138.3575426923.550859158.4494229713.436846252.6171719711.07364513.59044953.5620980no